{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 22: Intro to Matplotlib through a simple scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "people = ['Ann','Brandon','Chen','David','Emily','Farook',\n",
    "          'Gagan','Hamish','Imran','Joseph','Katherine','Lily']\n",
    "age = [21,12,32,45,37,18,28,52,5,40,48,15]\n",
    "weight = [55,35,77,68,70,60,72,69,18,65,82,48]\n",
    "height = [160,135,170,165,173,168,175,159,105,171,155,158]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa09cde48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(age,weight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa0a54588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"Plot of Age vs. Weight (in kgs)\",fontsize=20)\n",
    "plt.xlabel(\"Age (years)\",fontsize=16)\n",
    "plt.ylabel(\"Weight (kgs)\",fontsize=16)\n",
    "plt.grid (True)\n",
    "plt.ylim(0,100)\n",
    "plt.xticks([i*5 for i in range(12)],fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.scatter(x=age,y=weight,c='orange',s=150,edgecolors='k')\n",
    "plt.text(x=20,y=85,s=\"Weights are more or less similar \\nafter 18-20 years of age\",fontsize=15)\n",
    "plt.vlines(x=20,ymin=0,ymax=80,linestyles='dashed',color='blue',lw=3)\n",
    "plt.legend(['Weight in kgs'],loc=2,fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 23: Generating random numbers from a Uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randint(1,10)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randint(1,10,size=1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 3 4 2 1 5 1 1 4 4]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randint(1,6,size=10)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[58.09 52.55 75.67 97.25 85.69 82.1  76.61 84.91 56.61 52.12 94.76 70.18\n",
      " 93.25 75.51 51.22]\n"
     ]
    }
   ],
   "source": [
    "x = 50+50*np.random.random(size=15)\n",
    "x= x.round(decimals=2)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.22970354 0.0465693  0.05575691]\n",
      " [0.57112177 0.37055141 0.62719709]\n",
      " [0.82848353 0.06978211 0.39281706]]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.rand(3,3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 24: Generating random numbers from a Binomial distribution and Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 4 4 4 7 7 6 5]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.binomial(10,0.6,size=8)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa2ab4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "plt.title(\"Number of successes in coin toss\",fontsize=16)\n",
    "plt.bar(left=np.arange(1,9),height=x)\n",
    "plt.xlabel(\"Experiment number\",fontsize=15)\n",
    "plt.ylabel(\"Number of successes\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 25: Generating random numbers from Normal distribution and Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9121603716396331\n"
     ]
    }
   ],
   "source": [
    "x = np.random.normal()\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa2bfaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heights = np.random.normal(loc=155,scale=10,size=100)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(heights,color='orange',edgecolor='k')\n",
    "plt.title(\"Histogram of teen aged students's height\",fontsize=18)\n",
    "plt.xlabel(\"Height in cm\",fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 26: Calculation of descriptive statistics from a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>People</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ann</td>\n",
       "      <td>21</td>\n",
       "      <td>55</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chen</td>\n",
       "      <td>32</td>\n",
       "      <td>77</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>45</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emily</td>\n",
       "      <td>37</td>\n",
       "      <td>70</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Farook</td>\n",
       "      <td>18</td>\n",
       "      <td>60</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gagan</td>\n",
       "      <td>28</td>\n",
       "      <td>72</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hamish</td>\n",
       "      <td>52</td>\n",
       "      <td>69</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Imran</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>48</td>\n",
       "      <td>82</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lily</td>\n",
       "      <td>15</td>\n",
       "      <td>48</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       People  Age  Weight  Height\n",
       "0         Ann   21      55     160\n",
       "1     Brandon   12      35     135\n",
       "2        Chen   32      77     170\n",
       "3       David   45      68     165\n",
       "4       Emily   37      70     173\n",
       "5      Farook   18      60     168\n",
       "6       Gagan   28      72     175\n",
       "7      Hamish   52      69     159\n",
       "8       Imran    5      18     105\n",
       "9      Joseph   40      65     171\n",
       "10  Katherine   48      82     155\n",
       "11       Lily   15      48     158"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_dict={'People':people,'Age':age,'Weight':weight,'Height':height}\n",
    "people_df=pd.DataFrame(data=people_dict)\n",
    "people_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.416666666666668\n"
     ]
    }
   ],
   "source": [
    "print(people_df['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175\n"
     ]
    }
   ],
   "source": [
    "print(people_df['Height'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.45120510148239\n"
     ]
    }
   ],
   "source": [
    "print(people_df['Weight'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.25"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(people_df['Age'],25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inter-quartile range:  24.0\n"
     ]
    }
   ],
   "source": [
    "pcnt_75 = np.percentile(people_df['Age'],75)\n",
    "pcnt_25 = np.percentile(people_df['Age'],25)\n",
    "print(\"Inter-quartile range: \",pcnt_75-pcnt_25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Age     Weight      Height\n",
      "count  12.000000  12.000000   12.000000\n",
      "mean   29.416667  59.916667  157.833333\n",
      "std    15.329463  18.451205   19.834925\n",
      "min     5.000000  18.000000  105.000000\n",
      "25%    17.250000  53.250000  157.250000\n",
      "50%    30.000000  66.500000  162.500000\n",
      "75%    41.250000  70.500000  170.250000\n",
      "max    52.000000  82.000000  175.000000\n"
     ]
    }
   ],
   "source": [
    "print(people_df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 27: DataFrame even has built-in plotting utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa2b4eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "people_df['Weight'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaa2d72fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "people_df.plot.scatter('Weight','Height',s=150,c='orange',edgecolor='k')\n",
    "plt.grid(True)\n",
    "plt.title(\"Weight vs. Height scatter plot\",fontsize=18)\n",
    "plt.xlabel(\"Weight (in kg)\",fontsize=15)\n",
    "plt.ylabel(\"Height (in cm)\",fontsize=15)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
